Interactive evolution, i.e. leveraging
human input for selection in an evolutionary
algorithm, is effective
when an appropriate fitness function is
hard to quantify yet solution quality is
easily recognizable by humans.
However, single-user applications of interactive
evolution are limited
by user fatigue: Humans become
bored with monotonous evaluations.
This paper explores the potential
for bypassing such fatigue by
directly purchasing human input from
human computation markets.
Experiments evolving aesthetic images
show that purchased human input can be leveraged more economically
when evolution is first seeded by optimizing a purely-computational
aesthetic measure.
Further experiments in the same domain
validate a system feature,
demonstrating how human computation can help guide interactive evolution
system design.
Finally, experiments in an image composition domain show the
approach's potential to make interactive evolution
scalable even in tasks that are not inherently enjoyable. The conclusion is that
human computation markets make it possible to apply
a powerful
form of selection pressure mechanically in evolutionary algorithms.